Stockfish NNUE

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Stockfish NNUE Logo [1]

Stockfish NNUE,
a Stockfish branch by Hisayori Noda aka Nodchip, which uses Efficiently Updatable Neural Networks - stylized as ƎUИИ or reversed as NNUE - to replace its standard evaluation. NNUE, introduced in 2018 by Yu Nasu [2], were previously successfully applied in Shogi evaluation functions embedded in a Stockfish based search [3], such as YaneuraOu [4], and Kristallweizen [5]. YaneuraOu's author Motohiro Isozaki made an unbelievable prediction that NNUE can help to increase Stockfish strength by around 100 points, almost one year before revealing [6] [7]. In 2019, Nodchip incorporated NNUE into Stockfish 10 - as a proof of concept, and with the intention to give something back to the Stockfish community [8]. After support and announcements by Henk Drost in May 2020 [9] and subsequent enhancements, Stockfish NNUE was established and recognized. In summer 2020, with more people involved in testing and training, the computer chess community bursts out enthusiastically due to its rapidly raising playing strength with different networks trained using a mixture of supervised and reinforcement learning methods. Despite the approximately halved search speed, Stockfish NNUE became stronger than its original [10].

In August 2020, Fishtest revealed Stockfish NNUE was stronger than the classical one at least 80 Elo [11]. In July 2020, the playing code of NNUE was put into the official Stockfish repository as a branch for further development and examination. In August that playing code merged to the master branch and become an official part of the engine. However, the training code still remained in Nodchip's repository [12] [13]. On September 02, 2020, Stockfish 12 was released with a huge jump in playing strength due to the introduction of NNUE and further tuning [14].

NNUE Structure

The neural network consists of four layers. The input layer is heavily overparametrized, feeding in the board representation for all king placements per side. The efficiency of NNUE is due to incremental update of the input layer outputs in make and unmake move, where only a tiny fraction of its neurons need to be considered [15]. The remaining three layers with 2x256x32, 32x32 and 32x1 weights are computational less expensive, hidden layer 1 and 2 with ReLu activation [16] [17], best calculated using appropriate SIMD instructions performing fast 8-bit/16-bit integer vector arithmetic, like MMX, SSE2 or AVX2 on x86/x86-64, or, if available, AVX-512.


NNUE layers in action [18]

Nodchip explained on Discord on June 2020 [19]:

41,024 = 64 * 641. 64 comes from the number of the cells where king may exist. 641 = 64 * 5 * 2 + 1. 64 here comes from the number of the cells where a piece other than king may exist. 5 is the number of piece types other than king. 2 is the number of the colors, white and black. 1 is a captured piece.
Here "bona" means "bonanza" which is a popular computer shogi engine. It introduced the feature "p" for the first time.
BonaPieces are contained in the evalList. It is updated by Position::do_move() and Position::undo_move(), and used by NNUE to calculate the network parameters between the input layer and the first hidden layer.
About the calculation, the following text will be helpful. This text is sent to RocketMiningPoo on Twitter.
"We add the i-th COLUMN of the W{0} to the z{0} for each i, where the i-th element is set to 1. And we subtract the i-th COULMN of the W{0} from the z{0} for each i, where the i-th element is set to 0. This operation is "accumulate" in your question." may be right... I hope that someone will double check.


All networks are built by some volunteers but not by any big community (differs from Leela Chess Zero) and can be uploaded into Fishtest for testing. Networks with good test results will be released officially on Fishtest website [20].

After long discussing the best way to publish networks with Stockfish [21], the developing team decided to embed the default network into Stockfish binaries, making sure NNUE always works as well as bringing more convenience to users.


In August 2020 a new patch changed Stockfish NNUE into a hybrid engine: it uses NNUE evaluation only on quite balanced material positions, otherwise uses the classical one. It could speed up to 10% and gain 20 Elo [22]. At that point, NNUE helped to increase already around 100 Elo for Stockfish. In the same month, Stockfish changed the default mode of using evaluation functions from classic to hybrid one, the last step to completely accept NNUE.

Strong Points

For Users

  • Runs with CPU only, and doesn't require expensive video cards, as well the need for installing drivers and 3rd specific libraries. Thus it is much easier to install (compared to engines using deep convolutional neural networks, such as Leela Chess Zero) and suitable for almost all modern computers. Using a GPU is even not practical for that small net - host-device-latency aka. kernel-launch-overhead [23] [24] to a then underemployed GPU are not sufficient for the intended NPS range [25]
  • Releases with only one network (via UCI options), that help to delete users' confusion from finding, selecting and setting up. The network is selected carefully from Fishtest

For Developers

  • Requires small training sets. Some high score networks can be built with the effort of one or a few people within a few days. It doesn't require the massive computing from a supercomputer and/or from community
  • Doesn’t require complicated systems such as a sophisticated client-server model to train networks. Just a single binary from Nodchip’ repo is enough to train
  • The NNUE code is independent and can be separated easily from the rest and integrated to other engines [26]

Being attracted by new advantages as well as being encouraged by some impressive successes, many developers joined or continued to work. The Official Stockfish repository shows the numbers of commits, ideas increased significantly after merging NNUE.

Rotation vs Flip

Since the 9x9 Shogi board has a centered king file and castling is not known in Shogi, color flip versus180 degree rotate differs in a horizontal mirrored position from the other side's point of view, with otherwise identical playing options. The NNUE is trained and probed from the side to move point of view, where the used 180 degree rotation (xor 63 instead of 56) to flip sides looks rather strange for chess [27]. i.e color flipping the black king from e8 to d1 rather than e1. Does it consider castling short to the queen side?

It is a little bit unclear, how that rotation rather than flip affects the playing strength [28] and whether NNUE for chess suffers from associative visual agnosia. Maybe Fishtest needs to play many games with color-flipped openings, i.e. 1.e4 e5 and 1.e3 e5 2.e4, to look whether results differ or not. Anyway, a fix from rotate to flip has to be done from producer and consumer sides, and is likely to void some training sessions.


In reply to Unai Corzo, Motohiro Isozaki aka Yaneurao, suggested 3 techniques that applied successfully to Shogi and can be brought back to Stockfish NNUE and may improve it another 100 - 200 Elo [29] [30]:

See also

Forum Posts

2020 ...

January ...


Re: NNUE accessible explanation by Jonathan Rosenthal, CCC, July 23, 2020
Re: NNUE accessible explanation by Jonathan Rosenthal, CCC, July 24, 2020
Re: NNUE accessible explanation by Jonathan Rosenthal, CCC, August 03, 2020


Re: this will be the merge of a lifetime : SF 80 Elo+ by Henk Drost, CCC, August 04, 2020


October ...

Re: NNUE Question - King Placements by syzygy, CCC, October 23, 2020
Re: NNUE Question - King Placements by syzygy, CCC, October 23, 2020

External Links







  1. Stockfish NNUE Logo from GitHub - nodchip/Stockfish: UCI chess engine by Nodchip
  2. Yu Nasu (2018). ƎUИИ Efficiently Updatable Neural-Network based Evaluation Functions for Computer Shogi. Ziosoft Computer Shogi Club, pdf (Japanese with English abstract)
  3. The Stockfish of shogi by Larry Kaufman, CCC, January 07, 2020
  4. GitHub - yaneurao/YaneuraOu: YaneuraOu is the World's Strongest Shogi engine(AI player), WCSC29 1st winner, educational and USI compliant engine
  5. GitHub - Tama4649/Kristallweizen: 第29回世界コンピュータ将棋選手権 準優勝のKristallweizenです。
  6. 将棋ソフト開発者がStockfishに貢献する日 The day when shogi software developers contribute to Stockfish by Motohiro Isozaki, June 2019
  7. shogi engine developer claims he can make Stockfish stronger, Reddit, August 2019
  8. Stockfish NNUE – The Complete Guide, June 19, 2020 (Japanese and English)
  9. Stockfish NN release (NNUE) by Henk Drost, CCC, May 31, 2020
  10. Can the sardine! NNUE clobbers SF by Henk Drost, CCC, July 16, 2020
  11. Introducing NNUE Evaluation, August 06, 2020
  12. NNUE merge · Issue #2823 · official-stockfish/Stockfish · GitHub by Joost VandeVondele, July 25, 2020
  13. GitHub - nodchip/Stockfish: UCI chess engine by Nodchip
  14. Stockfish 12, The Stockfish Team, Stockfish Blog, September 02, 2020
  15. Re: NNUE accessible explanation by Jonathan Rosenthal, CCC, July 23, 2020
  16. Stockfish/halfkp_256x2-32-32.h at master · official-stockfish/Stockfish · GitHub
  17. Stockfish/clipped_relu.h at master · official-stockfish/Stockfish · GitHub
  18. Image courtesy Roman Zhukov, revised version of the image posted in Re: Stockfish NN release (NNUE) by Roman Zhukov, CCC, June 17, 2020, labels corrected October 23, 2020, see Re: NNUE Question - King Placements by Andrew Grant, CCC, October 23, 2020
  19. Don't understand NNUE by Lucasart, CCC, August 14, 2020
  20. Neural Net download and statistics
  21. Improve dealing with the default net? Issue ##3030 · official-stockfish/Stockfish · GitHub by Joost VandeVondele, August 19, 2020
  22. NNUE evaluation threshold by MJZ1977 · Pull Request #2916 · official-stockfish/Stockfish · GitHub, August 06, 2020
  23. AB search with NN on GPU... by Srdja Matovic, CCC, August 13, 2020 » GPU
  24. kernel launch latency - CUDA / CUDA Programming and Performance - NVIDIA Developer Forums by LukeCuda, June 18, 2018
  25. stockfish with graphics card by h1a8, CCC, August 06, 2020
  26. NNUE Engines
  27. Stockfish/types.h at 615d98da2447e79ceceae205e0cd4e878115acc3 · official-stockfish/Stockfish · GitHub
  28. NNUE eval rotate vs mirror · Issue #3021 · official-stockfish/Stockfish · GitHub by Terje Kirstihagen, August 17, 2020
  29. 3 technologies in shogi AI that could be used for chess AI, Motohiro Isozaki, August 2020
  30. GitHub - NNUE ideas and discussion (post-merge). #2915, August 2020
  31. An info by Sylwy, CCC, July 25, 2020
  32. You can now look inside NNUE and look at its Per square value estimation by Henk Drost, CCC, August 04, 2020

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